# search.py
# ---------
# Licensing Information:  You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
# 
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).


"""
In search.py, you will implement generic search algorithms which are called by
Pacman agents (in searchAgents.py).
"""

import util


class SearchProblem:
    """
    This class outlines the structure of a search problem, but doesn't implement
    any of the methods (in object-oriented terminology: an abstract class).

    You do not need to change anything in this class, ever.
    """

    def getStartState(self):
        """
        Returns the start state for the search problem.
        """
        util.raiseNotDefined()

    def isGoalState(self, state):
        """
          state: Search state

        Returns True if and only if the state is a valid goal state.
        """
        util.raiseNotDefined()

    def getSuccessors(self, state):
        """
          state: Search state

        For a given state, this should return a list of triples, (successor,
        action, stepCost), where 'successor' is a successor to the current
        state, 'action' is the action required to get there, and 'stepCost' is
        the incremental cost of expanding to that successor.
        """
        util.raiseNotDefined()

    def getCostOfActions(self, actions):
        """
         actions: A list of actions to take

        This method returns the total cost of a particular sequence of actions.
        The sequence must be composed of legal moves.
        """
        util.raiseNotDefined()


def tinyMazeSearch(problem):
    """
    Returns a sequence of moves that solves tinyMaze.  For any other maze, the
    sequence of moves will be incorrect, so only use this for tinyMaze.
    """
    from game import Directions
    s = Directions.SOUTH
    w = Directions.WEST
    return [s, s, w, s, w, w, s, w]


def dfs_inner(problem, point, visited: list, road: list):
    to_points = problem.getSuccessors(point)
    for next in to_points:
        if next[0] not in visited:
            road.append(next[1])
            visited.append(next[0])
            if problem.isGoalState(next[0]):
                return road
            else:
                res = dfs_inner(problem, next[0], visited, road)
                if res == None:
                    visited.pop()
                else:
                    return res
            road.pop()
    return None


def depthFirstSearch(problem):
    """
    Search the deepest nodes in the search tree first.

    Your search algorithm needs to return a list of actions that reaches the
    goal. Make sure to implement a graph search algorithm.

    To get started, you might want to try some of these simple commands to
    understand the search problem that is being passed in:

    # print("Start:", problem.getStartState())
    # print("Is the start a goal?", problem.isGoalState(problem.getStartState()))
    # print("Start's successors:", problem.getSuccessors(problem.getStartState()))
    """
    "*** YOUR CODE HERE ***"

    # print("Start:", problem.getStartState())
    # print("Is the start a goal?", problem.isGoalState(problem.getStartState()))
    # print("Start's successors:", problem.getSuccessors(problem.getStartState()))
    startPoint = problem.getStartState();
    visited = [startPoint]
    road = []
    result = dfs_inner(problem, startPoint, visited, road)
    return result
    util.raiseNotDefined()


class Node:
    parent = None
    point = None
    par2pot = ''
    #到达该点的实际开销
    cost = 0

    def __init__(self, point, parent=None, par2point=None, cost=0):
        self.point = point
        self.parent = parent
        self.par2pot = par2point
        self.cost = cost


def getRoad(node: Node):
    result = []
    while node.parent is not None:
        result.append(node.par2pot)
        node = node.parent
    list.reverse(result)
    return result


def breadthFirstSearch(problem: SearchProblem):
    """Search the shallowest nodes in the search tree first."""
    start_point = problem.getStartState()
    open_list = [Node((start_point, None, None))]
    visited = []
    while len(open_list) > 0:
        n = open_list.pop(0)
        if problem.isGoalState(n.point[0]):
            return getRoad(n)
        if n.point[0] not in visited:
            visited.append(n.point[0])
            points = problem.getSuccessors(n.point[0])
            for next in points:
                if next[0] not in visited:
                    node = Node(next, parent=n, par2point=next[1])
                    open_list.append(node)
    return None


def uniformCostSearch(problem):
    """Search the node of least total cost first."""
    "*** YOUR CODE HERE ***"
    from util import PriorityQueue
    start_point = problem.getStartState()

    # open_list = [Node((start_point, None, None))]
    open_queue = PriorityQueue()
    open_queue.push(Node(point=(start_point, None, None), parent=None, par2point=None, cost=0),
                    priority=0)
    visited = []
    while not open_queue.isEmpty():
        n = open_queue.pop()
        if problem.isGoalState(n.point[0]):
            return getRoad(n)
        if n.point[0] not in visited:
            visited.append(n.point[0])
            points = problem.getSuccessors(n.point[0])
            g_pre = n.cost
            for next in points:
                if next[0] not in visited:
                    g = g_pre + next[2]
                    node = Node(next, parent=n, par2point=next[1], cost=g)
                    open_queue.push(node, priority=g)
    return None


def nullHeuristic(state, problem=None):
    """
    A heuristic function estimates the cost from the current state to the nearest
    goal in the provided SearchProblem.  This heuristic is trivial.
    """
    return 0


# 这是个可以配置启发函数的A*算法（模板）
def aStarSearch(problem, heuristic=nullHeuristic):
    """Search the node that has the lowest combined cost and heuristic first."""
    from util import PriorityQueue
    start_point = problem.getStartState()

    # open_list = [Node((start_point, None, None))]
    open_queue = PriorityQueue()
    open_queue.push(Node(point=(start_point,None,None), parent=None, par2point=None, cost=0),priority=heuristic(start_point,problem))
    visited = []
    while not open_queue.isEmpty():
        n = open_queue.pop()
        if problem.isGoalState(n.point[0]):
            return getRoad(n)
        if n.point[0] not in visited:
            visited.append(n.point[0])
            points = problem.getSuccessors(n.point[0])
            g_pre = n.cost
            for next in points:
                if next[0] not in visited:
                    g=g_pre+next[2]
                    f = g+heuristic(next[0],problem)
                    node = Node(next, parent=n, par2point=next[1],cost=g)
                    open_queue.push(node,priority=f)
    return None
    util.raiseNotDefined()


# Abbreviations
bfs = breadthFirstSearch
dfs = depthFirstSearch
astar = aStarSearch
ucs = uniformCostSearch
